1. Field of the Invention
The present invention relates to a noise reduction apparatus, a noise reduction method, programs and media used for image equipment such as, for example, televisions, videos or the like.
2. Related Art of the Invention
A noise reduction apparatus for achieving high image quality in conventional video equipment is referred to herein.
For such noise reduction apparatuses, there are two types; one is a three-dimensional (3-D) noise reduction apparatus for achieving noise reduction based upon differences between signal fields or frames using memories, etc., and the other is a two-dimensional (2-D) noise reduction apparatus for achieving noise reduction based upon signals in the same field or frame.
Additionally, the 2-D noise reduction apparatus is broadly grouped into two types, one uses a nonlinear type filter such as a median filter and the other uses a spatial low pass filter (LPF), and the latter one will be referred to herein.
The 2-D spatial LPF reduces noise having spatial high frequency components by performing LPF processing in the horizontal direction and the vertical direction of image signals, however if the LPF processing is simply performed, deterioration of the image quality such as a dull-edge and resolution degradation is generated because the high frequency components are attenuated in a edge portion and a detail portion of the image.
Therefore, a 2-D adaptive type LPF referred to hereafter is devised in order to prevent deterioration of the image quality. FIG. 19 is a block diagram showing one example of the 2-D adaptive type LPF.
Simultaneous processing means 101 in FIG. 19 comprises a 2-D signal block consisting of a plurality of pixels based upon image signals inputted from an input terminal S1.
One example of such a signal block is shown in FIG. 20. FIG. 20 shows a case where the signal block consists of 13 pixels, and subscripts of the lower-right of a character “a” designate the locations. Where, ai,j is called a processing object pixel and pixels excluding ai,j are called peripheral pixels located around the periphery of the processing object pixel ai,j.
In addition, one example of the simultaneous processing means required for forming the signal block in FIG. 20 is shown in FIG. 21. In FIG. 21, reference numerals 201a and 201b designate 1H (H: horizontal scanning period) delay elements and reference numerals 202a–202o designate 1T (T: horizontal sampling period) delay elements respectively.
Subtracting means 1021–102n (in the case of the signal block in FIG. 20, n=12) output differences obtained after subtracting a value of the processing object pixel ai,j from values of the peripheral pixels excluding the processing object pixel ai,j in FIG. 20.
Each correlation detector 1031–103n compares the output value from the subtracting means 1021–102n with a pregiven threshold value and, if the output value is lower than the threshold value, outputs a level “1” by determining that there is a correlation between the processing object pixel ai,j and the peripheral pixel excluding the processing object pixel ai,j, if not, a level “0” is outputted.
Counting means 104 counts the number of “1”s appeared in the outputs of the correlation detectors 1031–103n, i.e., the number of peripheral pixels determined to be in correlation with the processing object pixel ai,j, and outputs the value as a numeric value to be a divisor in average value processing. Further, the counting means 104 outputs location information about the peripheral pixels determined to be in correlation with the processing object pixel ai,j as well.
Selection means 105 selects all the differences between the peripheral pixels determined to be in correlation with the processing object pixel ai,j and the processing object pixel ai,j out of the outputs from the subtracting means 1021–102n according to the location information about the peripheral pixels determined to be in correlation with the processing object pixel ai,j outputted from the counting means 104 and the processing object pixel ai,j, and outputs them to a first adding means 106 without processing then.
By taking a specific example using the signal block shown in FIG. 20, if four peripheral pixels ai−1,j, ai,j−1, ai,j+1 and ai+1,j, for example, are determined to be in correlation with the processing object pixel ai,j, the selection means 105 outputs each of the following four differences to the first adding means 106 because it is required to average four differences of (ai−1,j−ai,j), (ai,j−1−ai,j), (ai,j+1−ai,j) and (ai+1,j, j−ai,j).
The first adding means 106 calculates a total sum of the outputs from the selection means 105 and inputs it into dividing means 107. The dividing means 107 calculates an average value of the differences between the peripheral pixels and the processing object pixel by dividing the total sum of the outputs from the first adding means 106 by a numeric value to be a divisor in the averaging processing, outputted from the counting means 104, e.g., in the above specific example, the divisor is “4”.
Second adding means 108 adds the average value of the differences between the processing object pixel and the peripheral pixels, which is the output from the dividing means 107, to the value of the processing object pixel ai,j from the simultaneous processing means 101.
According to the above configuration, it is meant the average value of the processing object pixel and the peripheral pixels to be in correlation with it is calculated in the 2-D adaptive type LPF, resulting in noise reduction. This will be referred to hereafter.
Supposing b1 is a value of the processing object pixel, b2–bn are values of the peripheral pixels correlating with it and noise with levels c1–cn are superimposed on these pixels. In addition, supposing all square average values c2 of the noise levels superimposed on each pixel have the same value. It is meant an operation of an “arithmetic expression 1” is performed in this 2-D adaptive type LPF.b1+{(b2−b1)+ . . . +(bn−b1)}/n  (Arithmetic expression 1)
Here, an “arithmetic expression 2” is obtained by transforming the “arithmetic expression 1”.
                                          b            1                    +                                    {                                                (                                                            b                      2                                        -                                          b                      1                                                        )                                +                …                +                                  (                                                            b                      n                                        -                                          b                      1                                                        )                                            }                        /            n                          =                                            {                                                nb                  1                                +                                  (                                                            b                      2                                        -                                          b                      1                                                        )                                +                …                +                                  (                                                            b                      n                                        -                                          b                      1                                                        )                                            }                        /            n                    =                                    (                                                b                  1                                +                                  b                  2                                +                …                +                                  b                  n                                            )                        /            n                                              (                  Arithmetic          ⁢                                          ⁢          expression          ⁢                                          ⁢          2                )                                                      (                                          c                1                2                            +                              c                2                2                            +              …              +                              c                n                2                                      )                                              1              /              2                        n                          =                                            n                                                1                  /                  2                                ⁢                c                                      ⁢                          c              /              n                                =                      c            /                          n                              1                /                2                                                                        (                  Arithmetic          ⁢                                          ⁢          expression          ⁢                                          ⁢          3                )            
By performing an operation on the “arithmetic expression 1”, i.e., the “arithmetic expression 2”, the noise level becomes 1/n1/2 as shown in the “arithmetic expression 3”, resulting in noise reduction.
Further, the dull-edge and detail deterioration can be reduced in this 2-D adaptive type LPF. This manner is shown in FIG. 22(a) and FIG. 22(b).
FIG. 22(a) and FIG. 22(b) show cases where the processing of the 2-D adaptive type LPF is performed at edge portions. FIG. 22(a) shows a case of a horizontal edge and FIG. 22(b) shows a case of a vertical edge.
Halftone portions in the drawing show low intensity portions 221 and the other portions show high intensity portions 222 respectively.
Now, supposing a difference between the low intensity portion 221 and the high intensity portion 222 (contrast) in the drawing is sufficiently higher than a threshold value in the correlation detectors 1031–103n, and the noise level superimposed on each pixel is lower than the threshold value described above, in the case of FIG. 22(a), the dull-edge is not generated because five pixels ai−1,j−1, ai,j−3, ai,j−2, ai,j−1 and ai+1,j−1 existing in the low intensity portion 221 are excluded from the averaging processing, and an average value of remaining eight pixels existing in the high intensity portion 222 is calculated.
It is the same as a case of FIG. 22(b). Further, the detail is not impaired even in the detail portion because the detail which is higher than the threshold value described above is omitted from the averaging processing.
Regarding the details having lower value than the threshold value set in the correlation detector, however, the detail deterioration is generated in the above configuration because the averaging processing is performed. This will be explained hereafter.
FIG. 23 shows frequency characteristics wherein six pixels in the front and the back of the processing object pixel ai,j are determined to be in correlation with it in the 2-D adaptive type LPF according to the configuration described above. Where, fsh represents a horizontal sampling frequency. As can be seen from the chart, signals lower than the threshold value set in the correlation detectors are completely suppressed in a frequency band equal to or higher than fsh/8, regardless of the noise components or the signal components.
Accordingly, if the detail with a low amplitude exists in the signal components, the deterioration becomes significant.
Additionally, since the 2-D adaptive type LPF described above has characteristics to reduce noise in a high frequency band more than noise in a low frequency band, there is a problem that noise with large grains in a low frequency band remains to be obstructive. This will be explained hereafter.
FIG. 24 shows an example of image signals S1 inputted into the 2-D adaptive type LPF. In the case of such input signals, if the threshold value set in the correlation detectors of the 2-D adaptive type LPF is 3, ai,j−3 and ai,j+3 out of the six pixels in the front and the back of the processing object pixel ai,j are omitted from the averaging processing because the differences between the pixels ai,j−3 and ai,j+3 and the processing objective pixel become 5 and 4 respectively, consequently the averaging processing is performed using four pixels in the front and the back of the processing object pixel ai,j.
Now, the frequency characteristics in this case are represented in FIG. 25. It is understood from the chart that the noise levels in a low frequency band around fsh/8 are not much suppressed when compared to the frequency characteristics wherein the forward and backward six pixels are determined to be in correlation, and a degree of noise reduction within this frequency band becomes lower.
Accordingly, the conventional 2-D adaptive type LPF has the characteristics wherein the noise in a low frequency band is reduced only when there are close correlations with peripheral pixels around the processing object pixel, so that the noise in a low frequency band is easy to remain, further, there is a problem that grains of noise in a low frequency band are larger than those in a high frequency band, resulting in obstruction.